Artificial Signals Research Articles

An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19.

At present, the Corona Virus Disease 2019 (COVID-19) is ravaging the world, bringing great impact on people’s life safety and health as well as the healthy development of economy and society, so the research on the prediction of the development trend of the epidemic is crucial. In this paper, we focus on the prevention and control of epidemic using the relevant technologies in the field of artificial intelligence and signal analysis. With the unknown principle of epidemic transmission, we first smooth out the complex and variable epidemic data through the empirical mode decomposition model to obtain the change trends of epidemic data at different time scales. On this basis, the change trends under different time scales are trained using an extreme learning machine to obtain the corresponding prediction values, and finally the epidemic prediction results are obtained by fitting through Adaptive Network-based Fuzzy Inference System. The experimental results show that the algorithm has good learning ability, especially in the prediction of time-series sequences can guarantee the accuracy rate while having low time complexity. Therefore, this paper not only plays a theoretical support for epidemic prevention and control, but also plays an important role in the construction of public emergency health system in the long run.

Chaotic and stochastic dynamics of epileptiform-like activities in sclerotic hippocampus resected from patients with pharmacoresistant epilepsy.

The types of epileptiform activity occurring in the sclerotic hippocampus with highest incidence are interictal-like events (II) and periodic ictal spiking (PIS). These activities are classified according to their event rates, but it is still unclear if these rate differences are consequences of underlying physiological mechanisms. Identifying new and more specific information related to these two activities may bring insights to a better understanding about the epileptogenic process and new diagnosis. We applied Poincaré map analysis and Recurrence Quantification Analysis (RQA) onto 35 in vitro electrophysiological signals recorded from slices of 12 hippocampal tissues surgically resected from patients with pharmacoresistant temporal lobe epilepsy. These analyzes showed that the II activity is related to chaotic dynamics, whereas the PIS activity is related to deterministic periodic dynamics. Additionally, it indicates that their different rates are consequence of different endogenous dynamics. Finally, by using two computational models we were able to simulate the transition between II and PIS activities. The RQA was applied to different periods of these simulations to compare the recurrences between artificial and real signals, showing that different ranges of regularity-chaoticity can be directly associated with the generation of PIS and II activities.

Acoustical testing of face coverings using an artificial acoustic head with speech audio

The COVID-19 pandemic has caused many communities to require or at least recommend the wearing of face coverings, masks, and/or shields inside indoor public spaces to reduce the transmission rate of the virus. Several types of face coverings are studied, including standardized N95 and KN95, assorted cloth, and clear plastic face shields. They are evaluated on an acoustical head and torso simulator (HATS) setup in a classroom with two different locations for monitoring via a sound level meter. The HATS is used as a controlled and repeatable artificial voice or sound source, which reproduces the ITU-T artificial speech signals as well as playback of real speech signals for American English, male and female voices. The signals are recorded with a sound level meter at a distance of 2 and 6 m between source and receiver in a classroom environment. These signals evaluated in time and frequency domains to better understand the acoustical difference between the no-mask and various masks conditions. These are compared with the same classroom environment but using white noise test signals.

The Effect of Kinesthetic and Artificial Tactile Noise and Variability on Stiffness Perception.

Robot-assisted minimally invasive surgeries (RAMIS) have many benefits. A disadvantage, however, is the lack of haptic feedback. Haptic feedback is comprised of kinesthetic and tactile information, and we use both to form stiffness perception. Applying both kinesthetic and tactile feedback can enable more precise feedback than kinesthetic feedback alone. However, during remote surgeries, haptic noises and variations can be present. Therefore, toward designing haptic feedback for RAMIS, it is important to understand the effect of haptic manipulations on stiffness perception. We assessed the effect of two manipulations using stiffness discrimination tasks in which participants received force feedback and artificial skin stretch. In Experiment 1, we added sinusoidal noise to the artificial tactile signal, and found that the noise did not affect participants' stiffness perception or uncertainty. In Experiment 2, we varied either the kinesthetic or the artificial tactile information between consecutive interactions with an object. We found that the both forms of variability did not affect stiffness perception, but kinesthetic variability increased participants' uncertainty. We show that haptic feedback, comprised of force feedback and artificial skin stretch, provides robust haptic information even in the presence of noise and variability, and hence can potentially be both beneficial and viable in RAMIS.

Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal

Fractal algorithms for signal analysis are developed from geometric fractals and can be used to describe various complex signals in nature. A roughness scaling extraction algorithm with first-order flattening (RSE-f1) was shown in our previous studies to have a high accuracy, strong noise resistance, and a unique capacity to recognize the complexity of non-fractals that are common in signals. In this study, its disadvantage of a long calculation duration was addressed by using a dichotomy-binary strategy. The accelerated RSE-f1 algorithm (A-RSE-f1) retains the three above-mentioned advantages of the original algorithm according to theoretical analysis and artificial signal testing, while its calculation speed is significantly accelerated by 13 fold, which also makes it faster than the typical Higuchi algorithm. Afterwards, the vibration signals of the milling process are analyzed using the A-RSE-f1 algorithm, demonstrating the ability to distinguish different machining statuses (idle, stable, and chatter) effectively. The results of this study demonstrate that the RSE algorithm has been improved to meet the requirements of practical engineering with both a fast speed and a high performance.

A Study on Biocompatible Polymer-Based Packaging of Neural Interface for Chronic Implantation.

This paper proposed and verified the use of polymer-based packaging to implement the chronic implantation of neural interfaces using a combination of a commercial thermal epoxy and a thin parylene film. The packaging’s characteristics and the performance of the vulnerable interface between the thermal epoxy layer and polyimide layer, which is mainly used for neural electrodes and an FPCB, were evaluated through in vitro, in vivo, and acceleration experiments. The performance of neural interfaces—composed of the combination of the thermal epoxy and thin parylene film deposition as encapsulation packaging—was evaluated by using signal acquisition experiments based on artificial stimulation signal transmissions through in vitro and in vivo experiments. It has been found that, when commercial thermal epoxy normally cured at room temperature was cured at higher temperatures of 45 °C and 65 °C, not only is its lifetime increased with about twice the room-temperature-based curing conditions but also an interfacial adhesion is higher with more than twice the room-temperature-based curing conditions. In addition, through in vivo experiments using rats, it was confirmed that bodily fluids did not flow into the interface between the thermal epoxy and FPCB for up to 18 months, and it was verified that the rats maintained healthy conditions without occurring an immune response in the body to the thin parylene film deposition on the packaging’s surface.

Application of a Modified Empirical Wavelet Transform Method in VLF/LF Lightning Electric Field Signals

In this paper, to realize a better adaptive method for the lightning electric field signal denoising, we firstly compared the decomposition results of three methods called the EMD (empirical mode decomposition), the CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), and the EWT (empirical wavelet transform) by artificial signals, respectively, and found that the EWT was better than the other two methods. Then, a MEWT (modified empirical wavelet transform) method based on the EWT was presented for processing the natural lightning signals data. By using our MEWT method, we processed three types of electric field signal data with different frequency bands radiated by the lightning step leader, the cloud pulse and the return stroke, respectively, and the VLF (very low frequency) lightning signals propagating different distances from 500 km to 3500 km, by using the data of the fast electric field change sensors from Nanjing Lightning Location Network (NLLN) in 2018 and the data of the fast electric field change sensors and the VLF electric antennas from the NUIST Wide-range Lightning Location System (NWLLS) in 2021. The results showed that our presented MEWT method could adaptively process different lightning signal data with different frequencies from the step leader, the cloud pulse, and the return stroke; for the lightning VLF signal data from 500 km to 3500 km, the MEWT also achieved a better noise reduction effect. After denoising the signal by using our MEWT, the detection ability of the fast electric field change sensor was improved, and more weak lightning signals could be identified.

Characteristics of ULF Magnetic Fields in the 3D Inhomogeneous Earth‐Ionosphere Waveguide

AbstractWe develop an algorithm for calculating the characteristics of the magnetic field of signals in the frequency range of (0.1–20) Hz in the horizontally and vertically inhomogeneous spherical Earth‐ionosphere waveguide. This algorithm involves calculations of the surface impedance of an inhomogeneous ionosphere. The frequency dependences of the amplitude, phase and polarization of the magnetic field generated by a horizontal magnetic dipole were analyzed. It is shown that the horizontal inhomogeneity of the Earth‐ionosphere waveguide can significantly affect the frequency dependence of the amplitude and polarization of the magnetic field, increasing the degree of circular polarization. The horizontal inhomogeneity only slightly influences the results in daytime conditions. The most noticeable difference in the amplitude dependences of the magnetic components and large phase shifts (up to 40°) are observed when the terminator passes through the geodetic line connecting the source and receiver, and in the presence of sporadic layers with a horizontally inhomogeneous structure along the low‐frequency wave propagation path. We compare the calculation results with the magnetic pulsations recorded at Nurmijarvi station during the 2001 campaigns of Kola Peninsula ultra low frequency emitter operation. The obtained results make it possible to qualitatively explain peculiarities of the amplitude and polarization spectra of the artificial magnetic signals recorded in this campaigns that are not explained within the framework of the flat homogeneous waveguide model.

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry.

The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the handcrafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on thousands of samples of individual cell types, which limit the system in terms of induction of a new cell type for auto classification or characterization. Further, its performance suffers from poor image (cell diffraction pattern) signatures due to their small signal or background noise. In this work, we address these issues by leveraging the artificial intelligence-powered auto signal enhancing scheme such as denoising autoencoder and adaptive cell characterization technique based on the transfer of learning in deep neural networks. The performance of our proposed method shows an increase in accuracy >98% along with the signal enhancement of >5 dB for most of the cell types, such as red blood cell (RBC) and white blood cell (WBC). Furthermore, the model is adaptive to learn new type of samples within a few learning iterations and able to successfully classify the newly introduced sample along with the existing other sample types.

Orbital Artifacts in Multi‐GNSS Precise Point Positioning Time Series

AbstractThe time series of station coordinates derived using Global Navigation Satellite Systems (GNSS) are affected by several technique errors, influencing the studies of the geophysical processes and phenomena. Using different GNSS constellations leads to the appearance of various artificial signals with amplitudes up to several millimeters. The presence of the GNSS system‐specific artifacts is demonstrated using the 2‐year long series of station coordinates and zenith total delay parameters for 15 stations using Precise Point Positioning algorithms. The most dominant orbital artifacts for Galileo appear with periods of 14.08 hr, 17.09 hr, 34.20 hr, 2.49 days, and ∼3.4 days. The corresponding signals for GLONASS appear with periods of 5.63 hr, 7.36 hr, 10.64 hr, 21.26 hr, 3.99 days, and ∼8 days. Moreover, when estimating discrete 24‐hr solutions from high‐rate GNSS data, high‐frequency signatures are under‐sampled, resulting in long‐term aliased periodic signals. Galileo is less vulnerable to the orbital signals than GPS or GLONASS. The difference is visible mainly for the East coordinate. The Galileo‐based daily estimates are up to 55% and 36% better than those delivered from GLONASS and GPS. The GPS orbital signals arise at the periods corresponding to the harmonics of the K1 tide, which leads to the inconsistency of the GPS‐based products with geophysical models reaching on average 12 mm for the K2 tidal term in the Up component. The magnitude of the orbital signals depends on the geographical location of the station and the geometry of the satellites' flybys. Finally, using a combination of GPS and Galileo increases the precision of estimates by 10% compared with the best‐case Galileo‐only solution.

Dark Energy Survey Year 3 results: galaxy clustering and systematics treatment for lens galaxy samples

ABSTRACT In this work, we present the galaxy clustering measurements of the two DES lens galaxy samples: a magnitude-limited sample optimized for the measurement of cosmological parameters, maglim, and a sample of luminous red galaxies selected with the redmagic algorithm. maglim/redmagic sample contains over 10 million/2.5 million galaxies and is divided into six/five photometric redshift bins spanning the range z ∈ [0.20, 1.05]/z ∈ [0.15, 0.90]. Both samples cover 4143 $\deg ^2$ over which we perform our analysis blind, measuring the angular correlation function with an S/N ∼ 63 for both samples. In a companion paper, these measurements of galaxy clustering are combined with the correlation functions of cosmic shear and galaxy–galaxy lensing of each sample to place cosmological constraints with a 3 × 2pt analysis. We conduct a thorough study of the mitigation of systematic effects caused by the spatially varying survey properties and we correct the measurements to remove artificial clustering signals. We employ several decontamination methods with different configurations to ensure the robustness of our corrections and to determine the systematic uncertainty that needs to be considered for the final cosmology analyses. We validate our fiducial methodology using lognormal mocks, showing that our decontamination procedure induces biases no greater than 0.5σ in the (Ωm, b) plane, where b is the galaxy bias.

Intrinsic Mode Cross Correlation: A Novel Technique to Identify Scale-Dependent Lags Between Two Signals and Its Application to Ionospheric Science

In this work, we address the following question: can we use modern, cutting-edge techniques conceived for the analysis of nonlinear non-stationary signals to measure scale-wise lags? To this scope, we propose a novel technique, called intrinsic mode cross correlation (IMXC) method, which leverages on the decomposition of nonlinear non-stationary signals by the multivariate fast iterative filtering (MvFIF) technique and the computation of a scale-by-scale cross correlation. We evaluate this technique on artificial signals (whose ground truth is known) and plasma density data provided by the Langmuir probes onboard the Swarm satellites. We show that this technique allows indeed to reconstruct the lag dependence on the involved spatio/temporal scales for the artificial dataset (even in the presence of high levels of noise) and to estimate them in a real-life signal. This can pave the way to future uses of this technique in contexts in which the causation chain can be hidden in a complex, multiscale coupling of the investigated features.

Supramolecularly regulated artificial transmembrane signal transduction for 'ON/OFF'-switchable enzyme catalysis.

An artificial signal transduction model with a supramolecular recognition headgroup, a membrane anchoring group, and a pro-enzyme catalysis endgroup was constructed. The transmembrane translocation of the transducer can be reversibly regulated by competitive host-guest complexations as an input signal to control an enzyme reaction inside the lipid vesicles.

Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

A system for artificial light signal transduction via molecular translocation in a lipid membrane.

Light signal transduction pathways are the central components of mechanisms that regulate plant development, in which photoreceptors receive light and participate in light signal transduction. Chemical systems can be designed to mimic these biological processes that have potential applications in smart sensing, drug delivery and synthetic biology. Here, we synthesized a series of simple photoresponsive molecules for use as photoreceptors in artificial light signal transduction. The hydrophobic structures of these molecules facilitate their insertion into vesicular lipid bilayers, and reversible photoisomerization initiates the reciprocating translocation of molecules in the membrane, thus activating or deactivating the hydrolysis reaction of a precatalyst in the transducer for an encapsulated substrate, resulting in a light controllable output signal. This study represents the first example of using simplified synthetic molecules to simulate light signal transduction performed by complex biomolecules.

Comparison of commercial and original methods for denoising electrical waveforms with constant or linearly variable magnitudes

Acquired electrical waveforms can be affected by white noise. The 1-st part of the paper analysis deals with the denoising of multi-period steady signals by using 3 methods: mean signal method, an original method relying on wavelet packet trees and the method implemented by the wavelet-based Matlab function wden. The signal length influence over the mean signal method’s accuracy is studied. The results yielded by the other 2 methods are also analyzed considering signals with 7 periods. Afterward the wavelet-based methods are used to denoise segments of 7 periods with linearly variable magnitudes (ascending or descending) for 3 different slopes. Artificial test signals, with rich harmonic content, were used. They were polluted by sets of 10 white noises with different powers. Maximum absolute deviations and mean square root deviations were computed considering the original signals, before pollution, versus the corresponding denoised signal. The metrics were computed relative to the maximum absolute value of the noise and allowed to determine the most accurate method.

Multivariate Fast Iterative Filtering for the Decomposition of Nonstationary Signals

In this work, we present a new technique for the decomposition of multivariate data, which we call Multivariate Fast Iterative Filtering (MvFIF) algorithm. We study its properties, proving rigorously that it converges in finite time when applied to the decomposition of any kind of multivariate signal. We test MvFIF performance using a wide variety of artificial and real multivariate signals, showing its ability to: separate multivariate modulated oscillations; align frequencies along different channels; produce a quasi–dyadic filterbank when decomposing white Gaussian noise; decompose the signal in a quasi–orthogonal set of components; being robust to noise perturbation, even when the number of channels is increased considerably. Finally, we compare it and its performance with the main methods developed so far in the literature, proving that MvFIF produces, without any a priori assumption on the signal under investigation and in a fast and reliable manner, a uniquely defined decomposition of any multivariate signal.

Adaptive Recursive Variational Mode Decomposition for Multiple Engine Faults Detection

Engine fault detection is critical to enhancing the reliability of modern equipment. However, it is challenging to obtain a large number of high-quality labeled data for engines, which is not conducive to improving the training accuracy of deep learning methods. Therefore, this article proposes a fault detection method combining adaptive recursive variational mode decomposition (ARVMD) and component energy distribution spectrum (CEDS). The paper first introduces recursive mode into VMD. Then, the mode number is dynamically selected according to the energy distribution of the power spectral density to extract the intrinsic mode functions (IMFs) continuously. The quadratic penalty term is optimized correspondingly using SNR. The decomposition results of artificial and real signals demonstrated that ARVMD has higher SNR and efficiency than VMD. Next, the center frequency and unit bandwidth energy of IMF are used to construct CEDS. Fault diagnosis is realized by the CEDS correlation ranking of various faults. Finally, two case studies are performed to illustrate the effectiveness of the proposed method. The results show that the proposed ARVMD-CEDS method provides an efficient and effective solution for single-channel engine fault diagnosis.

25-річний досвід створення та впровадження інтелектуальних інформаційних технологій оброблення біометричних сигналів складної форми

Introduction. An important area of modern information technology application is medical diagnostics, which is based on computer processing of biomedical signals The purpose of the article is to provide information on the results of basic and applied research that has ensured the practical implementation of the ECG method (fasegraphy method) in various fields of application and to outline further prospects for these studies. Methods. The technology is based on a stochastic model of generating an artificial signal of complex shape in terms of internal and external distortions. Results. It is shown that the efficiency in extracting diagnostic information from biomedical signals in conditions of the real distortions, which are not always additive in nature, can be increased by switching from a scalar signal in the time domain to a cognitive image in the phase plane. Original algorithms of adaptive filtering and smoothing have been developed, which made it possible to obtain a numerical estimate of the first derivative of the distorted signal. Recovery of the useful signal (reference cycle) for distorted implementations is carried out by averaging the phase trajectories with the subsequent return to the time domain. To increase the reliability of additional diagnostic features of the ECG in the phase space is proposed and clinical data have proven their usefulness in terms of reducing the risk of misdiagnosis. The practical results of the implementation of the diagnostic complex FASEGRAF® have confirmed the effectiveness of fasegraphy in various fields of application. Plans for further prospective research are presented. Conclusions. Continuation of research allow to create new competitive information technologies and digital medicine devices.